How Twitter Inc (TWTR) Processes Complex Mobile Application Data Each Day?
Introducing a fresh tool has become part and parcel of the social site including Twitter Inc (NYSE:TWTR). However, when the tool is related to the processing of complex mobile application data, it becomes a tough task to handle on a day-to-day basis.
How Twitter Inc (NYSE:TWTR) manages its Answers tool is naturally a big question that everyone is keen to know. The tool was launched only seven months ago and is designed to offer users the analytics of mobile applications. The company has been witnessing approximately five billion daily sessions since then.
Millions Of Events
The company’s Answers tool gets millions of events each second from millions of gadgets every day. This was undoubtedly a big amount of data to be processed. The company has provided details in a blog as to how it configured its structure to manage the tough task. Twitter indicated that it was able to develop a backbone of Answers to manage the mobile application data. It included also how it was received, archived or processed in real time. Finally, they are processed in large pieces, which were also called batch processing.
Twitter Inc (NYSE:TWTR) indicated that every time the Answer tool is used to find out how the individual’s mobile app is functioning, the company registers and condenses that data. That data is then put into batches. This is done to protect the battery power of the device without applying enormous strain on its network, which routes that data to the company’s servers from the mobile device.
The company indicated that the data flows into a Kafka queue, which can be utilized as a short-term place to store them. Such stored data then passes through Amazon Simple Storage Service or Amazon S3 to where the company preserves the data in a permanent location.
Twitter Inc (NYSE:TWTR) said that it uses Storm to route the data flows into Kafka and then write it to Amazon S3. The company will also use Amazon Elastic MapReduce for batch processing of the data stored in Amazon S3.
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